MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization

TL;DR

This paper introduces a multiobjective deep reinforcement learning algorithm enhanced with evolutionary learning to effectively solve complex multiobjective vehicle routing problems with time windows, outperforming existing methods.

Contribution

It presents a novel multiobjective DRL framework with decomposition, context integration, and evolutionary fine-tuning for complex routing problems.

Findings

The proposed method outperforms existing learning-based approaches.

It effectively handles multiobjective vehicle routing with time windows.

Experimental results demonstrate superior solution quality.

Abstract

Learning-based heuristics for solving combinatorial optimization problems has recently attracted much academic attention. While most of the existing works only consider the single objective problem with simple constraints, many real-world problems have the multiobjective perspective and contain a rich set of constraints. This paper proposes a multiobjective deep reinforcement learning with evolutionary learning algorithm for a typical complex problem called the multiobjective vehicle routing problem with time windows (MO-VRPTW). In the proposed algorithm, the decomposition strategy is applied to generate subproblems for a set of attention models. The comprehensive context information is introduced to further enhance the attention models. The evolutionary learning is also employed to fine-tune the parameters of the models. The experimental results on MO-VRPTW instances demonstrate the superiority of the proposed algorithm over other learning-based and iterative-based approaches.